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Parsimonious Modeling of Snow Accumulation and Snowmelt Processes in High Mountain Basins

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Parsimonious Modeling of Snow Accumulation and Snowmelt Processes in High Mountain Basins water Article Parsimonious Modeling of Snow Accumulation and Snowmelt Processes in High Mountain Basins Ismael Orozco 1,* , Félix Francés 2 and Jesús Mora 1 1 Departamento de Ingeniería Geomática e Hidráulica, Universidad de Guanajuato, Av. Juárez 77, Zona Centro, Guanajuato 36000, México; [email protected] 2 Research Institute of Water Engineering and Environment, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain; [email protected] * Correspondence: [email protected] Received: 16 March 2019; Accepted: 10 June 2019; Published: 20 June 2019 Abstract: The success of hydrological modeling of a high mountain basin depends in most case on the accurate quantification of the snowmelt. However, mathematically modeling snowmelt is not a simple task due to, on one hand, the high number of variables that can be relevant and can change significantly in space and, in the other hand, the low availability of most of them in practical engineering. Therefore, this research proposes to modify the original equation of the classical degree-day model to introduce the spatial and temporal variability of the degree-day factor. To evaluate the effects of the variability in the hydrological modeling and the snowmelt modeling at the cell and hillslope scale. We propose to introduce the spatial and temporal variability of the degree-day factor using maps of radiation indices. These maps consider the position of the sun according to the time of year, solar radiation, insolation, topography and shaded-relief topography. Our priority has been to keep the parsimony of the snowmelt model that can be implemented in high mountain basins with limited observed input. The snowmelt model was included as a new module in the TETIS distributed hydrological model. The results show significant improvements in hydrological modeling in the spring period when the snowmelt is more important. At cell and hillslope scale errors are diminished in the snowpack, improving the representation of the flows and storages that intervene in high mountain basins. Keywords: distributed degree-day snowmelt model; parsimonious hydrological modeling; TETIS model 1. Introduction The success of hydrologic modeling of high mountain basins depends in most cases on the correct quantification of snow accumulation and melting processes. According to [1], snow accumulation in winter as well as spring snowmelt gives to mountain catchments a particular hydrological response that should be taken into account when modeling river runoff. Also, mountain catchments are very sensible to temperature changes; therefore climate change can drastically impact the hydrological cycle [2,3]. For example, snowmelt is one of the processes intervening in the hydrological cycle and interacting with many other processes [4]. According to [5], climate change is likely to impact the seasonality and generation processes of floods, which has direct implications for flood risk assessment, design flood estimation, and hydropower production management. Therefore, it is very evident the importance of modeling the accumulation and snowmelt in mountain basins where a very high percentage of water comes from snow [4]. However, mathematically modeling of these processes is not an easy task due to the high spatial and temporal variability of the snow accumulation by itself and strong relationship of snowmelting and with precipitation, temperature, and orographic effects [6,7]. Water 2019, 11, 1288; doi:10.3390/w11061288 www.mdpi.com/journal/water Water 2019, 11, 1288 2 of 19 The study their relationship with runoff coming from snowmelt is a recent and recurrent topic [8–10]. These processes and relationships are traditionally modeled with mathematical simplifications that are based on simple approaches such as degree-day models and more complex conceptualizations such as energy balance models [11]. These mathematical simplifications are generally implemented as modules executed in hydrological conceptual rainfall-runoff models. There are recent publications that use models developed to implement the snow-runoff process (i.e., [12–15]). Unfortunately, the use of energy balance models is not the practical solution due to the high number of variables that can be relevant and can change significantly in space and, in the other hand, the low availability of most of them in a typical [16]. In addition, if used, most of the needed variables are indirectly estimated that increase the uncertainty in the results [16]. For the above mentioned reasons, we decided to the degree-day approach as an option for mountain catchments with limited observed input. According to [17] the classical degree-day model is the conceptualization most used for its parsimony and easy application in medium and large basins. Use only temperature and a spatially constant parameter denominated degree-day factor in their formulation, comprise one of these simplifications. However, according to Hock [18], these models incorrectly reproduce the spatial variability of the snowmelt, because the degree-day factor actually changes in time and space due to the influence of a series of variables such as: season, ground cover, topography, snow cover, snow contamination, atmospheric conditions and rainfall. Consequently, in recent decades there has been an attempt to introduce the variability of the degree-day factor using hybrid mathematical formulations that consider the distribution of the energy flow of longwave radiation (LWR) and shortwave radiation (SWR) as those proposed by [6,18–21]. Nevertheless, Hock [22] pointed out that there are two problems when using the distributed degree-day model: (a) despite working with long time periods, its precision diminishes when the temporal resolution increases and; (b) the spatial variability is not modeled with precision, because the snowmelt rates can vary substantially influenced by topographic effects such as hill shade, slope and orientation. For these reasons, our research introduces the spatial and temporal variability of the degree-day factor, using a new modification of the equation of the classical degree-day method with different approaches that will be compared. Moreover, we evaluate the effects of the variability in the hydrological modeling and the snowmelt modeling at the cell and slope scale in two high mountain basins. In this research, we decided to implement the new snowmelt formulation in the distributed hydrological model TETIS, with physically-based parameters developed by the Research Group of Hydrological and Environmental Modeling of the Polytechnic University of Valencia, Spain. The TETIS model simulates the main processes of the hydrological cycle through a conceptualization of tanks [23]. The TETIS model is a free software available at http://lluvia.dihma.upv.es/ES/software/ software.html. This model has been implemented in a high number of basins in Spain [24] and Latin America [25], and also there are applications in France [26], USA [27], UK [28], and China [29]. 2. Case Studies The basins selected for the case studies in this paper are the Carson River and the American River basins located in the Sierra Nevada, between the states of California and Nevada in the United States (longitude 118◦–124◦ W and latitude 38◦–40◦ N) (Figure1). These two basins have also been used in the Distributed Hydrologic Model Intercomparison Project-Phase 2 (DMIP2) directed by the National Weather Service of the National Oceanic and Atmospheric Administration [30] Water 2019, 11, 1288 3 of 19 Figure 1. Location of the Carson and American basins in Sierra Nevada, USA, including the streamflow gauges ( ) and SNOTEL stations (s). Despite being geographically quite close, the hydrologic regimes of these basins are very different due to the average elevation and the location of the dividing line of the Sierra Nevada [31]. The Carson River basin has an area of 922 km2 and is has an altitude of between 1548 m and 3408 m. This basin presents a hydrologic regime completely dominated by snow. The American River basin has an area of 886 km2 and has a lower altitude than that of the Carson, between 281 m and 2630 m. The hydrologic regime of this basin is mixed, influenced by rain and snow. According to NFDC1, GRDN2, and CMEC1 streamflow gauges (Figure1), the most important runoffs in the basins are registered in spring and summer. The Carson River basin registers a mean annual accumulated precipitation of between 559 mm and 1244 mm, and the American River basin records a mean annual accumulated precipitation of between 813 mm and 1651 mm [32]. Furthermore, the mean annual temperature of the Carson River basin varies between 0 ◦C to 14 ◦C, while that of the American River basin is between 3 ◦C and 18 ◦C[32]. 3. Methodology To perform a better hydrological and snowmelt modeling of high mountain basins with limited observed input, this paper proposes a methodology that consists of several phases (Figure2). In hydrological modeling we use the TETIS model, which is a conceptual model with physically-based parameters [23]. The TETIS model is described in detail later. On the other hand, in snowmelt modeling we use the degree-day method. This method has shown good results in a high number of basins which vary in size [33] and is widely used because it requires little information and it is easily adaptable to rainfall-runoff models. The choice of this method is basically due to the shortage of complete Water 2019, 11, 1288 4 of 19 information of net radiation, sensible energy, latent energy, soil heat and energy advection. This information is necessary to estimate the snowmelt using the models which consider the energy balance applying the conservation laws [34]. For the foregoing, we propose a modification to the original equation of the degree-day model, to introduce the variability of the degree-day factor. The models are automatically calibrated and are compared with the models of the Distributed Hydrologic Model Intercomparison Project-Phase 2 (DMIP2), of the National Weather Service (NWS) of the National Oceanic and Atmospheric Administration (NOAA) [30]. Figure 2. Methodology implemented in the parsimonious modeling of high mountain basins.
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